Autonomous Fruit Picking Robot for Table Top Growing Systems
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Solution Overview
Problem
Current robotic fruit picking systems are expensive, require human operators, and are not compatible with European table top growing systems, leading to high production costs and labor market fluctuations, making them inefficient for commercial use.
Innovation Solution
A robotic fruit picking system with autonomous positioning, a picking arm, computer vision for image analysis, a control subsystem for learning picking strategies, a quality control subsystem for grading, and a storage subsystem for efficient fruit handling and storage, utilizing lower-cost off-the-shelf components and state-of-the-art computer vision techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sophisticated hardware and naive robot control systems are used for robotic fruit picking, then picking capability is achieved, but cost increases and compatibility with table top growing systems is lost
Solution Approach 1:
The robotic system is designed with universal end effectors and adjustable mechanical components that can adapt to multiple growing systems including table top systems used in Europe, allowing one system to serve multiple functions and configurations without requiring specialized hardware for each system type
Solution Approach 2:
The robot employs dynamic control systems with real-time adjustment capabilities that allow the mechanical arms and end effectors to adapt their motion patterns and gripping forces based on the specific growing system configuration, enabling compatibility across different systems without hardware changes
2Productivity
If large machines with sophisticated hardware are used, then picking capability is achieved, but production cost per unit picking capacity increases
Solution Approach 1:
The robotic system is divided into modular components including separate end effectors, mechanical arms, and control units that can be independently manufactured and assembled, allowing for optimized production scaling and reduced per-unit costs through standardized manufacturing of modular parts
Solution Approach 2:
The system uses computer vision and digital modeling to create virtual representations of the fruit and growing systems, allowing for simulation and optimization of picking strategies without requiring physical prototypes or trial-and-error hardware iterations, thereby reducing development and production costs
3Ease of operation
If human operators are used for fruit picking, then flexibility and adaptability are maintained, but labor costs fluctuate and consistency decreases
Solution Approach 1:
The robotic system incorporates computer vision subsystems that continuously capture images of the fruit and growing environment, providing real-time feedback to the control system for autonomous decision-making regarding fruit selection, ripeness assessment, and picking timing, ensuring consistent quality standards are met
Solution Approach 2:
The robot performs autonomous navigation, fruit identification, quality assessment, and picking operations without human intervention, with the control system independently processing visual data and executing picking decisions, thereby eliminating labor cost fluctuations and ensuring consistent operational standards
4Productivity
If expensive hardware and dated object recognition technology are used, then picking capability is achieved, but the system requires human operators for grading and post-processing
Solution Approach 1:
The system combines computer vision technology with integrated grading and quality control algorithms in a unified automated workflow, where the same vision subsystem that identifies fruit for picking also assesses quality metrics and determines grading categories, eliminating the need for separate human grading operations
Data Source
AI summary
A robotic fruit picking system includes an autonomous robot that includes a positioning subsystem that enables autonomous positioning of the robot using a computer vision guidance system. The robot also includes at least one picking arm and at least one picking head, or other type of end effector, mounted on each picking arm to either cut a stem or branch for a specific fruit or bunch of fruits or pluck that fruit or bunch. A computer vision subsystem analyses images of the fruit to be picked or stored and a control subsystem is programmed with or learns picking strategies using machine learning techniques. A quality control (QC) subsystem monitors the quality of fruit and grades that fruit according to size and/or quality. The robot has a storage subsystem for storing fruit in containers for storage or transportation, or in punnets for retail.